Parameters¶

PsyNeuLink parameters are objects that represent the user-modifiable parameters of a Component. Parameters have names, default values, and other attributes that define how they are used in Compositions. Parameter s also maintain and provide access to the data used in actual computations - default values, current values, previous values, and logged values.

Defaults¶

The Defaults class is used to represent the default values for a Component's parameters. Parameters have two types of defaults: instance defaults and class defaults. Class defaults belong to a PsyNeuLink class, and suggest valid types and shapes of Parameter values. Instance defaults belong to an instance of a PsyNeuLink class, and are used to validate compatibility between this instance and other PsyNeuLink objects. For example, given a TransferMechanism t:

instance defaults are accessible by t.defaults (e.g., for the noise parameter, t.defaults.noise.defaults.noise)

class defaults are accessible by t.class_defaults or TransferMechanism.defaults (e.g.,

t.class_defaults.noise or `TransferMechanism.defaults.noise)

Note

t.defaults.noise is shorthand for t.parameters.noise.default_value, and they both refer to the default noise value for t

Default values are sometimes also used when the parameters value has not been specified; for example, a Component’s defaults.variable is used as the input to a Mechanism if its execute method is called without any input specified, and similarly it is used for the INPUT Nodes of a Composition which are not specified in the inputs argument of its run method.

Statefulness of Parameters¶

Parameters can have different values in different execution contexts in order to ensure correctness of and allow access to simulation calculations. As a result, to inspect and use the values of a parameter, in general you need to know the execution context in which you are interested. Much of the time, this execution context is likely to be a Composition::

>>> import psyneulink as pnl
>>> c = pnl.Composition()
>>> d = pnl.Composition()
>>> t = pnl.TransferMechanism()
>>> c.add_node(t)
>>> d.add_node(t)

>>> c.run({t: 5})
[[array([5.])]]
>>> print(t.value)
[[5.]]

>>> d.run({t: 10})
[[array([10.])]]
>>> print(t.value)
[[10.]]

>>> print(t.parameters.value.get(c))
[[5.]]
>>> print(t.parameters.value.get(d))
[[10.]]

The TransferMechanism in the above snippet has a different value for each Composition it is run in. This holds true for all of its stateful Parameters, so they can behave differently in different execution contexts and can be modified by modulated Modulation.

Note

The “dot notation” version - t.value - refers to the most recent execution context in which t was executed. In many cases, you can use this to get or set using the execution context you’d expect. However, in complex situations, or if there is any doubt, it is best to explicitly specify the execution context using the parameter’s set method (for a more complete descritpion of the differences between dot notation and the set method, see Parameters.

Developers must keep in mind state when writing new Components for PsyNeuLink. Any parameters or values that may change during a run must become stateful Parameters, or they are at risk of computational errors like those encountered in parallel programming.

Creating Parameters¶

To create new Parameters, reference this example of a new class B

class B(A):
    class Parameters(A.Parameters):
        p = 1.0
        q = Parameter()

    def __init__(p=None, q=1.0):
        super(p=p, q=q)

create an inner class Parameters on the Component, inheriting from the parent Component’s Parameters class
an instance of B.Parameters will be assigned to the parameters attribute of the class B and all instances of B
each attribute on B.Parameters becomes a parameter (instance of the Parameter class)
- as with p, specifying only a value uses default values for the attributes of the Parameter
- as with q, specifying an explicit instance of the Parameter class allows you to modify the Parameter attributes
default values for the parameters can be specified in the Parameters class body, or in the arguments for B.__init__. If both are specified and the values differ, an exception will be raised
if you want assignments to parameter p to be validated, add a method _validate_p(value), that returns None if value is a valid assignment, or an error string if value is not a valid assignment
- NOTE: a validation method for p may reference other parameters only if they are listed in p’s dependencies
if you want all values set to p to be parsed beforehand, add a method _parse_p(value) that returns the parsed value
- for example, convert to a numpy array or float
  def _parse_p(value): return np.asarray(value)
- NOTE: parsers may not reference other parameters

setters and getters (used for more advanced behavior than parsing) should both return the final value to return (getter) or set (setter)

For example, costs of ControlMechanism has a special getter method, which computes the cost on-the-fly:

def _modulatory_mechanism_costs_getter(owning_component=None, context=None):
    try:
        return [c.compute_costs(c.parameters.variable._get(context), context=context) for c in owning_component.control_signals]
    except TypeError:
        return None

and matrix of RecurrentTransferMechanism has a special setter method, which updates its auto and hetero parameter values accordingly

def _recurrent_transfer_mechanism_matrix_setter(value, owning_component=None, context=None):
    try:
        value = get_matrix(value, owning_component.size[0], owning_component.size[0])
    except AttributeError:
        pass

    if value is not None:
        temp_matrix = value.copy()
        owning_component.parameters.auto._set(np.diag(temp_matrix).copy(), context)
        np.fill_diagonal(temp_matrix, 0)
        owning_component.parameters.hetero._set(temp_matrix, context)

    return value

Note

The specification of Parameters is intended to mirror the PNL class hierarchy. So, it is only necessary for each new class to declare Parameters that are new, or whose specification has changed from their parent’s. Parameters not present in a given class can be inherited from parents, but will be overridden if necessary, without affecting the parents.

Special Parameter Classes¶

FunctionParameter and SharedParameter are used to provide simpler access to some parameters of auxiliary components. They can be passed into the constructor of the owner, and then automatically passed when constructing the auxiliary component. The values of SharedParameters are shared via getter and with those of their target Parameter.

SharedParameters should only be used when there is a guarantee that their target will exist, given a specific Component. For example, it is acceptable that TransferMechanism.integration_rate is a FunctionParameter for the rate parameter of its integrator_function, because all TransferMechanisms have an integrator function, and all integrator functions have a rate parameter. It is also acceptable that ControlSignal.intensity_cost_function is a FunctionParameter corresponding to its function’s intensity_cost_fct parameter, because a ControlSignal’s function is always a TransferWithCosts and is not user-specifiable.

_user_specified¶

Currently, default argument values for __init__ methods of Components are all None to allow distinguishing between a default value a user actually passed in during construction and a default automatically assigned. Consider

pnl.TransferMechanism(noise=[[0, 0]])

pnl.TransferMechanism(default_variable=[[0]], noise=[[0, 0]])

In case 1, the default variable is not specified by the user, and is flexible as a result. Since noise indicates a shape, [[0, 0]] is implicitly assigned as the Mechanism’s default variable. This is intended as a convenience specification. In case 2, the variable and noise are incompatible, and an error is raised.

Using Parameters¶

Methods that are called during runtime in general must take context as an argument and must pass this context along to other PNL methods. The most likely place this will come up would be for the function method on a PNL Function class, or _execute method on other Components. Any getting and setting of stateful parameter values must use this context, and using standard attributes to store data must be avoided at risk of causing computation errors. You may use standard attributes only when their values will never change during a Run.

You should avoid using dot notation in internal code, as it is ambiguous and can potentially break statefulness.

Parameter attributes:

Attribute Name	Default value	Description	Dev notes
default_value	None	the default value of the Parameter
name	None	the name of the Parameter
stateful	True	whether the parameter has different values based on execution context
modulable	False	if True, the parameter can be modulated (if it belongs to a Mechanism or Projection it is assigned a ParameterPort)	Currently this does not determine what gets a ParameterPort, but in the future it should
read_only	False	whether the user should be able to set the value or not (e.g. variable and value are just for informational purposes).	Can be manually set, but will trigger a warning unless override=True
aliases	None	other names by which the parameter goes (e.g. allocation is the same as variable for ControlSignal).	specify as a list of strings
user	True	whether the parameter is something the user will care about (e.g. NOT context)
values	None	stores the parameter’s values under different execution contexts
getter	None	hook that allows overriding the retrieval of\|kwargs self, owning_component, and values based on a supplied method \|context will be passed in if your (e.g. _output_port_variable_getter) \|method uses them. self - the Parameter \|calling the setter; owning_component - \|the Component to which the Parameter \|belongs; context - the context \|the setter is called with; should return \|the value
setter	None	hook that allows overriding the setting of values based on a supplied method (e.g. _recurrent_transfer_mechanism_matrix_setter)	should take a positional argument; kwargs self, owning_component, and context will be passed in if your method uses them. self - the Parameter calling the setter; owning_component - the Component to which the Parameter belongs; context - the context the setter is called with; should return the value to be set
loggable	True	whether the parameter can be logged
log	None	stores the log of the parameter if applicable
log_condition	`OFF`	the `LogCondition` for which the parameter should be logged
history	None	stores the history of the parameter (previous values)
history_max_length	1	the maximum length of the stored history
fallback_default	False	if False, the Parameter will return None if a requested value is not present for a given execution context; if True, the Parameter’s default_value will be returned instead

Class Reference¶

class psyneulink.core.globals.parameters.Defaults(owner, **kwargs)¶

A class to simplify display and management of default values associated with the Parameters in a Parameters class.

With an instance of the Defaults class, defaults, defaults.<param_name> may be used to get or set the default value of the associated Parameters object

owner¶: the Parameters object associated with this object

values(show_all=False)¶

Parameters: show_all (False) – if True, includes non-user parameters
Returns: a dictionary with {parameter name: parameter value} key-value pairs corresponding to owner

class psyneulink.core.globals.parameters.Parameter(default_value=None, name=None, stateful=True, modulable=False, structural=False, modulation_combination_function=None, read_only=False, function_arg=True, pnl_internal=False, aliases=None, user=True, values=None, getter=None, setter=None, loggable=True, log=None, log_condition=LogCondition.OFF, delivery_condition=LogCondition.OFF, history=None, history_max_length=1, history_min_length=0, fallback_default=False, retain_old_simulation_data=False, constructor_argument=None, spec=None, parse_spec=False, valid_types=None, reference=False, dependencies=None, initializer=None, port=None, mdf_name=None, specify_none=False, _owner=None, _inherited=False, _inherited_source=None, _user_specified=False, **kwargs)¶

default_value¶

the default value of the Parameter.

Default: None

name¶

the name of the Parameter.

Default: None

stateful¶

whether the parameter has different values based on execution context.

Default: True

modulable¶

if True, the parameter can be modulated; if the Parameter belongs to a Mechanism or Projection, it is assigned a ParameterPort.

Default: False
Developer Notes: Currently this does not determine what gets a ParameterPort, but in the future it should

modulation_combination_function¶

specifies the function used in Port._get_combined_mod_val() to combine values for the parameter if it receives more than one ModulatoryProjections; must be either the keyword MULTIPLICATIVE, PRODUCT, ADDITIVE, SUM, or a function that accepts an n dimensional array and retursn an n-1 dimensional array. If it is None, the an attempt is made to determine it from the an alias for the Parameter’s name (i.e., if that is MULTIPLICATIVE_PARAM or ADDITIVE_PARAM); otherwise the default behavior is determined by Port._get_combined_mod_val().

Default: None

read_only¶

whether the user should be able to set the value or not (e.g. variable and value are just for informational purposes).

Default: False
Developer Notes: Can be manually set, but will trigger a warning unless override=True

function_arg¶

TBD

Default: False

pnl_internal¶

whether the parameter is an idiosyncrasy of PsyNeuLink or it is more intrinsic to the conceptual operation of the Component on which it resides

Default: False

aliases¶

other names by which the parameter goes (e.g. allocation is the same as variable for ControlSignal).

Type: list
Default: None
Developer Notes: specify as a list of strings

user¶

whether the parameter is something the user will care about (e.g. NOT context).

Default: True

values¶

stores the parameter’s values under different execution contexts.

Type: dict{execution_id: value}
Default: None

getter¶

hook that allows overriding the retrieval of values based on a supplied method (e.g. _output_port_variable_getter).

Type: types.FunctionType
Default: None
Developer Notes: kwargs self, owning_component, and context will be passed in if your method uses them. self - the Parameter calling the setter; owning_component - the Component to which the Parameter belongs; context - the context the setter is called with; should return the value

setter¶

hook that allows overriding the setting of values based on a supplied method (e.g. _recurrent_transfer_mechanism_matrix_setter).

Type: types.FunctionType
Default: None
Developer Notes: should take a positional argument; kwargs self, owning_component, and context will be passed in if your method uses them. self - the Parameter calling the setter; owning_component - the Component to which the Parameter belongs; context - the context the setter is called with; should return the value to be set

loggable¶

whether the parameter can be logged.

Default: True

log¶

stores the log of the parameter if applicable.

Type: dict{execution_id: deque([LogEntry])}
Default: None

log_condition¶

the LogCondition for which the parameter should be logged.

Type: LogCondition
Default: OFF

delivery_condition¶

the LogCondition for which the parameter shoud be delivered.

Type: LogCondition
Default: OFF

history¶

stores the history of the parameter (previous values). Also see get_previous.

Type: dict{execution_id: deque([LogEntry])}
Default: None

history_max_length¶

the maximum length of the stored history.

Default: 1

history_min_length¶

the minimum length of the stored history. generally this does not need to be overridden, but is used to indicate if parameter history is necessary to computation.

Default: 0

fallback_default¶

if False, the Parameter will return None if a requested value is not present for a given execution context; if True, the Parameter’s default_value will be returned instead.

Default: False

retain_old_simulation_data¶

if False, the Parameter signals to other PNL objects that any values generated during simulations may be deleted after they are no longer needed for computation; if True, the values should be saved for later inspection.

Default: False

constructor_argument¶

if not None, this indicates the argument in the owning Component’s constructor that this Parameter corresponds to.

Default: None

valid_types¶

if not None, this contains a tuple of types that are acceptable for values of this Parameter

Default: None

reference¶

if False, the Parameter is not used in computation for its owning Component. Instead, it is just meant to store a value that may be used to initialize other Components

Default: False
Developer Notes: Parameters with Function values marked as

reference will not be automatically instantiated in _instantiate_parameter_classes or validated for variable shape

dependencies¶

if not None, this contains a set of Parameter names corresponding to Parameters whose values must be instantiated before that of this Parameter

Default: None

initializer¶

the name of another Parameter that serves as this Parameter’s initializer

Default: None

port¶

stores a reference to the ParameterPort that modulates this Parameter, if applicable

Default: None

specify_none¶

if True, a user-specified value of None for this Parameter will set the _user_specified flag to True

Default: False

reset()¶: Resets default_value to the value specified in its Parameters class declaration, or inherits from parent Parameters classes if it is not explicitly specified.

get(context=None, **kwargs)¶

Gets the value of this Parameter in the context of context If no context is specified, attributes on the associated Component will be used

Parameters

context (Context, execution_id, Composition) – the context for which the value is stored; if a Composition, uses context.default_execution_id
kwargs – any additional arguments to be passed to this Parameter’s getter if it exists

get_previous(context=None, index=1, range_start=None, range_end=None)¶

Gets the value set before the current value of this Parameter in the context of context. To return a range of values, use range_start and range_end. Range takes precedence over Welcome to PsyNeuLink . All history values can be accessed directly as a list in Parameter.history.

Parameters

context – Context, execution_id, Composition the context for which the value is stored; if a Composition, uses context.default_execution_id
index (int) – how far back to look into the history. An index of 1 means the value this Parameter had just before its current value. An index of 2 means the value before that
range_start (Optional[int]) – Inclusive. The index of the oldest history value to return. If None, the range begins at the oldest value stored in history
range_end (Optional[int]) – Inclusive. The index of the newest history value to return. If None, the range ends at the newest value stored in history (does not include current value in Parameter.values)

Returns

the stored value or list of values in Parameter history

get_delta(context=None)¶

Gets the difference between the current value and previous value of Parameter in the context of context

Parameters: context (Context, execution_id, Composition) – the context for which the value is stored; if a Composition, uses context.default_execution_id

set(value, context=None, override=False, skip_history=False, skip_log=False, **kwargs)¶

Sets the value of this Parameter in the context of context If no context is specified, attributes on the associated Component will be used

Parameters

context (Context, execution_id, Composition) – the context for which the value is stored; if a Composition, uses context.default_execution_id
override (False) – if True, ignores a warning when attempting to set a read-only Parameter
skip_history (False) – if True, does not modify the Parameter’s history
skip_log (False) – if True, does not modify the Parameter’s log
kwargs – any additional arguments to be passed to this Parameter’s setter if it exists

clear_log(contexts=NotImplemented)¶: Clears the log of this Parameter for every context in contexts

clear_history(contexts=NotImplemented)¶

Clears the history of this Parameter for every context in contexts

Parameters: contexts (Union[Context, List[Context]]) –

class psyneulink.core.globals.parameters.ParameterAlias(source=None, name=None)¶: A counterpart to Parameter that represents a pseudo-Parameter alias that refers to another Parameter, but has a different name

exception psyneulink.core.globals.parameters.ParameterError¶

class psyneulink.core.globals.parameters.ParametersBase(owner, parent=None)¶

Base class for inner Parameters classes on Components (see Component.Parameters for example)

_get_prefixed_method(parse=False, validate=False, modulable=False, parameter_name=None)¶: Returns the parsing or validation method for the Parameter named parameter_name or for any modulable Parameter

psyneulink.core.globals.parameters.parse_context(context)¶

Parameters: context – An execution context (context, Composition)
Returns: the context associated with context

class psyneulink.core.globals.parameters.FunctionParameter(default_value=None, function_parameter_name=None, function_name='function', primary=True, **kwargs)¶

A special (and most common) case SharedParameter that references a Parameter on one of the Component’s functions.

function_parameter_name¶

the name of the target Parameter on the owning Component’s function_name Parameter

Type: str
Default: Parameter.name

function_name¶

the name of the owning Component’s Parameter on which function_parameter_name is the target Parameter of this object

Type: str
Default: ‘function’

class psyneulink.core.globals.parameters.SharedParameter(default_value=None, attribute_name=None, shared_parameter_name=None, primary=False, getter=None, setter=None, **kwargs)¶

A Parameter that is not a “true” Parameter of a Component but a reference to a Parameter on one of the Component’s attributes or other Parameters. Values are shared via getter and setter. Mainly used for more user-friendly access to certain Parameters, as a sort of cross-object alias.

See above for when it is appropriate to use a SharedParameter

shared_parameter_name¶

the name of the target Parameter on the owning Component’s attribute_name Parameter or attribute

Type: str
Default: Parameter.name

attribute_name¶

the name of the owning Component’s Parameter or attribute on which shared_parameter_name is the target Parameter of this object

Type: str
Default: ‘function’

primary¶

whether the default value specified in the SharedParameter should take precedence over the default value specified in its target

Type: bool
Default: False

getter¶

Type: types.FunctionType
Default: a function that returns the value of the shared_parameter_name parameter of the attribute_name Parameter/attribute of this Parameter’s owning Component

setter¶

Type: types.FunctionType
Default: a function that sets the value of the shared_parameter_name parameter of the attribute_name Parameter/attribute of this Parameter’s owning Component and returns the set value

get_previous(context=None, index=1, range_start=None, range_end=None)¶

Gets the value set before the current value of this Parameter in the context of context. To return a range of values, use range_start and range_end. Range takes precedence over Welcome to PsyNeuLink . All history values can be accessed directly as a list in Parameter.history.

Parameters

context – Context, execution_id, Composition the context for which the value is stored; if a Composition, uses context.default_execution_id
index (int) – how far back to look into the history. An index of 1 means the value this Parameter had just before its current value. An index of 2 means the value before that
range_start (Optional[int]) – Inclusive. The index of the oldest history value to return. If None, the range begins at the oldest value stored in history
range_end (Optional[int]) – Inclusive. The index of the newest history value to return. If None, the range ends at the newest value stored in history (does not include current value in Parameter.values)

Returns

the stored value or list of values in Parameter history

get_delta(context=None)¶

Gets the difference between the current value and previous value of Parameter in the context of context

Parameters: context (Context, execution_id, Composition) – the context for which the value is stored; if a Composition, uses context.default_execution_id